[silhouette-basic] Silhouette Plot for Cluster Quality Analysis

[MarkusNeusinger]

A silhouette plot showing the silhouette coefficient for each sample, grouped by cluster. Visualizes cluster cohesion and separation quality, helping to evaluate clustering results.

Applications:

• Evaluating K-means or hierarchical clustering quality
• Comparing different numbers of clusters
• Identifying poorly clustered samples
• Validating cluster assignments

Key elements:

• Horizontal bars for each sample's silhouette score
• Samples grouped by cluster with different colors
• Average silhouette score reference line
• Cluster-wise silhouette score annotations

[claude]

✅ Spec ID: silhouette-basic

Rationale: This is a distinct plot type for visualizing cluster quality via silhouette coefficients - not a duplicate of existing specs like heatmap-clustered or dendrogram-basic which focus on hierarchical relationships rather than cluster validation metrics.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: silhouette-basic

PR: #2336

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specification.md

silhouette-basic: Silhouette Plot

Description

A silhouette plot visualizes the quality of clustering results by showing the silhouette coefficient for each sample, grouped by cluster assignment. Each horizontal bar represents a sample's silhouette score (-1 to 1), where positive values indicate good cluster membership and negative values suggest potential misclassification. This visualization helps evaluate cluster cohesion (how similar samples are to their own cluster) and separation (how distinct they are from neighboring clusters).

Applications

• Evaluating K-means, hierarchical, or other clustering algorithm results
• Comparing different numbers of clusters to find optimal k value
• Identifying poorly clustered or potentially misclassified samples
• Validating cluster assignments before downstream analysis

Data

• samples (numeric) - feature vectors for each data point to be clustered
• cluster_labels (integer) - cluster assignment for each sample (0 to k-1)
• silhouette_values (numeric) - silhouette coefficient per sample (-1 to 1)
• Size: 50-500 samples with 2-10 clusters for readable visualization
• Example: clustering iris dataset into 3 species groups

Notes

• Display horizontal bars for each sample's silhouette score, sorted within each cluster
• Group samples by cluster with distinct colors per cluster
• Include vertical line at average silhouette score for reference
• Annotate each cluster section with its average silhouette score
• Use sklearn.metrics.silhouette_samples for computing individual scores
• Clusters with consistently high scores (close to 1) indicate well-separated groups

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Next: Add approved label to merge this specification to main.

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🤖 spec-create workflow

[MarkusNeusinger]

Completed: silhouette-basic implemented across all 9 libraries.